TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK
Anomaly Detection	One-class CIFAR-10	CSI	AUROC	94.3	# 1
Anomaly Detection	One-class CIFAR-100	CSI	AUROC	89.6	# 1
Anomaly Detection	One-class ImageNet-30	CSI	AUROC	91.6	# 1
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	CSI	AUROC	89.3	# 1

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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

NeurIPS 2020 • Jihoon Tack • Sangwoo Mo • Jongheon Jeong • Jinwoo Shin

Novelty detection, i.e., identifying whether a given sample is drawn from outside the training distribution, is essential for reliable machine learning. To this end, there have been many attempts at learning a representation well-suited for novelty detection and designing a score based on such representation... (read more)

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ANOMALY DETECTION

CONTRASTIVE LEARNING

OUT-OF-DISTRIBUTION DETECTION

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TASK	DATASET	MODEL	METRIC NAME	METRIC VALUE	GLOBAL RANK	BENCHMARK
Anomaly Detection	One-class CIFAR-10	CSI	AUROC	94.3	# 1	Compare
Anomaly Detection	One-class CIFAR-100	CSI	AUROC	89.6	# 1	Compare
Anomaly Detection	One-class ImageNet-30	CSI	AUROC	91.6	# 1	Compare
Anomaly Detection	Unlabeled CIFAR-10 vs CIFAR-100	CSI	AUROC	89.3	# 1	Compare

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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

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